[1]} formulated the memorization phenomenon by showing that a neural network with sufficient capacity can memorize completely random labels or random noise data (inputs) without substantially longer training time, and the explicit regularization is neither necessary nor sufficient to control the generalization error.
[2]} further varied the amount of random labels, and concluded that DNNs first learn general patterns of the real data before fitting the random data.
